Alpha Diversity
Data and R code accompanying:
Dominique G. Maucieri, Samuel Starko and Julia K. Baum
03 June, 2022
Summary of the Data
## [1] "total # of years"## [1] 6## [1] "total # of sites"## [1] 17## [1] "total # of site - year combinations"## [1] 34## [1] "total # of quadrats"## [1] 960Table S1
| Site | KI2013 | KI2014 | KI2015a | KI2015b | KI2016b | KI2017 |
|---|---|---|---|---|---|---|
| VL2 | 20 | 0 | 0 | 0 | 0 | 26 |
| VL1 | 0 | 0 | 28 | 0 | 30 | 0 |
| VL5 | 30 | 0 | 0 | 0 | 0 | 19 |
| L5 | 25 | 0 | 0 | 0 | 0 | 29 |
| M10 | 28 | 0 | 0 | 0 | 0 | 29 |
| L1 | 0 | 32 | 0 | 0 | 0 | 29 |
| H2 | 30 | 0 | 0 | 0 | 0 | 30 |
| VH3 | 29 | 0 | 0 | 0 | 30 | 0 |
| VH1 | 0 | 0 | 30 | 0 | 0 | 29 |
| VH2 | 0 | 0 | 0 | 30 | 0 | 30 |
| L4 | 25 | 0 | 0 | 0 | 0 | 30 |
| M3 | 0 | 0 | 0 | 30 | 0 | 29 |
| M2 | 26 | 0 | 0 | 0 | 30 | 0 |
| M1 | 0 | 0 | 0 | 30 | 0 | 29 |
| M4 | 0 | 21 | 0 | 0 | 0 | 30 |
| M6 | 28 | 0 | 0 | 0 | 30 | 0 |
| VL3 | 0 | 0 | 0 | 29 | 30 | 0 |
Coverage Standardizing
Figure S2
Extrapolation Trends
Figure S3
Hill Diversity
85% Coverage
Figure S4
90% Coverage
Figure 2
Figure S5
95% Coverage
Figure S6
Modelling
Table 1 and S3
- The following models were used to create Table 1 and S3
## [1] "Hill-Richness"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(order == 0)
##
## AIC BIC logLik deviance df.resid
## 142.1 155.9 -62.1 124.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 1.693 1.301
## Residual 1.126 1.061
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.13
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.009931 8.764425 2.511 0.01203 *
## poly(HD_Cont, 2)1 3.098374 2.913080 1.064 0.28751
## poly(HD_Cont, 2)2 -7.115000 2.395033 -2.971 0.00297 **
## MHWAfter -3.333235 0.363942 -9.159 < 2e-16 ***
## NPP -0.010909 0.008351 -1.306 0.19143
## poly(HD_Cont, 2)1:MHWAfter -3.996851 2.122129 -1.883 0.05964 .
## poly(HD_Cont, 2)2:MHWAfter -5.545241 2.122129 -2.613 0.00897 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Shannon"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(order == 1)
##
## AIC BIC logLik deviance df.resid
## 137.9 151.6 -59.9 119.9 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.6442 0.8026
## Residual 1.4467 1.2028
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.45
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 19.020068 6.825650 2.787 0.005327 **
## poly(HD_Cont, 2)1 1.590186 2.430764 0.654 0.512988
## poly(HD_Cont, 2)2 -6.996675 2.059513 -3.397 0.000681 ***
## MHWAfter -3.953707 0.412555 -9.583 < 2e-16 ***
## NPP -0.009310 0.006502 -1.432 0.152173
## poly(HD_Cont, 2)1:MHWAfter -1.327062 2.405589 -0.552 0.581183
## poly(HD_Cont, 2)2:MHWAfter -1.647390 2.405589 -0.685 0.493459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Simpson"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(order == 2)
##
## AIC BIC logLik deviance df.resid
## 132.9 146.6 -57.4 114.9 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.2001 0.4473
## Residual 1.5285 1.2363
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.53
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 15.863242 5.733000 2.767 0.005657 **
## poly(HD_Cont, 2)1 0.461803 2.162136 0.214 0.830869
## poly(HD_Cont, 2)2 -6.727069 1.870653 -3.596 0.000323 ***
## MHWAfter -4.128235 0.424063 -9.735 < 2e-16 ***
## NPP -0.007191 0.005460 -1.317 0.187788
## poly(HD_Cont, 2)1:MHWAfter 0.015729 2.472689 0.006 0.994924
## poly(HD_Cont, 2)2:MHWAfter 0.488883 2.472689 0.198 0.843270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Richness"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(order == 0)
##
## AIC BIC logLik deviance df.resid
## 159.5 173.2 -70.7 141.5 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 2.235 1.495
## Residual 2.134 1.461
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.13
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 27.48037 10.60354 2.592 0.00955 **
## poly(HD_Cont, 2)1 2.80018 3.59242 0.779 0.43570
## poly(HD_Cont, 2)2 -7.76270 2.98011 -2.605 0.00919 **
## MHWAfter -3.36594 0.50103 -6.718 1.84e-11 ***
## NPP -0.01449 0.01010 -1.434 0.15143
## poly(HD_Cont, 2)1:MHWAfter -3.80986 2.92146 -1.304 0.19220
## poly(HD_Cont, 2)2:MHWAfter -8.97593 2.92146 -3.072 0.00212 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Shannon"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(order == 1)
##
## AIC BIC logLik deviance df.resid
## 141.8 155.6 -61.9 123.8 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7324 0.8558
## Residual 1.6190 1.2724
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.62
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 21.193684 7.247612 2.924 0.003453 **
## poly(HD_Cont, 2)1 1.549540 2.578689 0.601 0.547905
## poly(HD_Cont, 2)2 -7.755289 2.184061 -3.551 0.000384 ***
## MHWAfter -4.303176 0.436427 -9.860 < 2e-16 ***
## NPP -0.010518 0.006904 -1.524 0.127621
## poly(HD_Cont, 2)1:MHWAfter -1.310906 2.544784 -0.515 0.606459
## poly(HD_Cont, 2)2:MHWAfter -2.030854 2.544784 -0.798 0.424844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Simpson"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(order == 2)
##
## AIC BIC logLik deviance df.resid
## 137.3 151.0 -59.6 119.3 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.1424 0.3773
## Residual 1.8179 1.3483
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.82
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.723613 5.986392 2.794 0.005212 **
## poly(HD_Cont, 2)1 0.484751 2.290866 0.212 0.832418
## poly(HD_Cont, 2)2 -7.387219 1.991615 -3.709 0.000208 ***
## MHWAfter -4.455059 0.462466 -9.633 < 2e-16 ***
## NPP -0.007462 0.005701 -1.309 0.190565
## poly(HD_Cont, 2)1:MHWAfter -0.073007 2.696620 -0.027 0.978401
## poly(HD_Cont, 2)2:MHWAfter 0.790979 2.696620 0.293 0.769276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Richness"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(order == 0)
##
## AIC BIC logLik deviance df.resid
## 190.4 204.2 -86.2 172.4 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.997 0.9985
## Residual 8.388 2.8962
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 8.39
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 33.20798 13.30160 2.497 0.012541 *
## poly(HD_Cont, 2)1 0.01762 5.03238 0.004 0.997207
## poly(HD_Cont, 2)2 -8.18759 4.35856 -1.879 0.060312 .
## MHWAfter -3.73394 0.99339 -3.759 0.000171 ***
## NPP -0.01732 0.01267 -1.367 0.171497
## poly(HD_Cont, 2)1:MHWAfter 1.04902 5.79243 0.181 0.856287
## poly(HD_Cont, 2)2:MHWAfter -15.73564 5.79243 -2.717 0.006596 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Shannon"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(order == 1)
##
## AIC BIC logLik deviance df.resid
## 146.2 159.9 -64.1 128.2 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7507 0.8664
## Residual 1.8970 1.3773
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.9
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.378736 7.608535 2.941 0.003269 **
## poly(HD_Cont, 2)1 1.092166 2.727785 0.400 0.688873
## poly(HD_Cont, 2)2 -8.269011 2.317231 -3.568 0.000359 ***
## MHWAfter -4.730051 0.472410 -10.013 < 2e-16 ***
## NPP -0.010678 0.007247 -1.473 0.140651
## poly(HD_Cont, 2)1:MHWAfter -0.575387 2.754601 -0.209 0.834540
## poly(HD_Cont, 2)2:MHWAfter -3.026929 2.754601 -1.099 0.271828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "Hill-Simpson"## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(order == 2)
##
## AIC BIC logLik deviance df.resid
## 142.0 155.8 -62.0 124.0 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.08229 0.2869
## Residual 2.16806 1.4724
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.17
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.839739 6.305676 2.671 0.007572 **
## poly(HD_Cont, 2)1 0.261876 2.444057 0.107 0.914671
## poly(HD_Cont, 2)2 -7.884811 2.133469 -3.696 0.000219 ***
## MHWAfter -4.790117 0.505041 -9.485 < 2e-16 ***
## NPP -0.007039 0.006004 -1.172 0.241097
## poly(HD_Cont, 2)1:MHWAfter 0.181635 2.944868 0.062 0.950819
## poly(HD_Cont, 2)2:MHWAfter 0.878027 2.944868 0.298 0.765585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Losses
Table S4
## # A tibble: 1 × 2
## HillRichness SEM_HillRichness
## <dbl> <dbl>
## 1 3.37 0.664## # A tibble: 1 × 2
## HillShannon SEM_HillShannon
## <dbl> <dbl>
## 1 4.30 0.462## # A tibble: 1 × 2
## HillSimpson SEM_HillSimpson
## <dbl> <dbl>
## 1 4.46 0.478| HD_Cat | HillRichness | SEM_HillRichness | HillShannon | SEM_HillShannon | HillSimpson | SEM_HillSimpson |
|---|---|---|---|---|---|---|
| Low | 1.991667 | 1.9410242 | 4.628667 | 1.3880636 | 5.334333 | 1.3185235 |
| Medium | 1.653500 | 1.0039179 | 3.679833 | 0.9446390 | 4.297167 | 0.9973579 |
| Very High | 5.862250 | 0.8116662 | 4.997750 | 0.4508515 | 4.390250 | 0.7816353 |
| Very Low | 4.469000 | 0.7238535 | 4.299500 | 1.0254281 | 4.097250 | 0.9799411 |
Stressor Responses
Note: Notation in code is the same as the notation used in the manuscript equations. ## Figure 4 (a-c) ### (a) Richness
##
## supF test
##
## data: fs.AR_Richness
## sup.F = 12.429, p-value = 0.03151##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Richness)
##
## Breakpoints at observation number:
## 9
##
## Corresponding to breakdates:
## 0.4705882##
## Call:
## lm(formula = ARi ~ HD_Cont, data = subset(AR_Richness, HD_Cont <=
## 34.82568))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31229 -0.10883 -0.04707 0.06764 0.39817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004617 0.116360 0.040 0.970
## HD_Cont -0.008649 0.005835 -1.482 0.189
##
## Residual standard error: 0.2363 on 6 degrees of freedom
## Multiple R-squared: 0.268, Adjusted R-squared: 0.146
## F-statistic: 2.197 on 1 and 6 DF, p-value: 0.1888##
## Call:
## lm(formula = ARi ~ HD_Cont, data = subset(AR_Richness, HD_Cont >=
## 34.82568))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41895 -0.08000 0.01754 0.11164 0.33289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.61544 0.22569 -2.727 0.0295 *
## HD_Cont 0.01097 0.00419 2.618 0.0345 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2296 on 7 degrees of freedom
## Multiple R-squared: 0.4947, Adjusted R-squared: 0.4225
## F-statistic: 6.854 on 1 and 7 DF, p-value: 0.03451
(b) Shannon
##
## supF test
##
## data: fs.AR_Shannon
## sup.F = 3.939, p-value = 0.7032##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Shannon)
##
## Breakpoints at observation number:
## 6
##
## Corresponding to breakdates:
## 0.2941176##
## Call:
## lm(formula = ARi ~ HD_Cont, data = AR_Shannon)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52140 -0.10413 0.00812 0.10876 0.40314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.012963 0.098560 0.132 0.897
## HD_Cont 0.001128 0.002374 0.475 0.642
##
## Residual standard error: 0.2419 on 15 degrees of freedom
## Multiple R-squared: 0.01482, Adjusted R-squared: -0.05086
## F-statistic: 0.2257 on 1 and 15 DF, p-value: 0.6416
(c) Simpson
##
## supF test
##
## data: fs.AR_Simpson
## sup.F = 2.872, p-value = 0.88##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Simpson)
##
## Breakpoints at observation number:
## 6
##
## Corresponding to breakdates:
## 0.2941176##
## Call:
## lm(formula = ARi ~ HD_Cont, data = AR_Simpson)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51523 -0.24739 0.06136 0.12391 0.48950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.030e-01 1.175e-01 0.877 0.395
## HD_Cont 7.178e-05 2.830e-03 0.025 0.980
##
## Residual standard error: 0.2883 on 15 degrees of freedom
## Multiple R-squared: 4.289e-05, Adjusted R-squared: -0.06662
## F-statistic: 0.0006433 on 1 and 15 DF, p-value: 0.9801